Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2

Learning from HIV-1 to predict the immunogenicity of T cell epitopes in SARS-COV-2

We describe a physics-based learning model for predicting the immunogenicity of Cytotoxic-T-Lymphocyte (CTL) epitopes derived from diverse pathogens including SARS-CoV-2. The model was trained and optimized on the relative immunodominance of CTL epitopes in Human Immunodeficiency Virus infection. It...

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Bibliographic Details
Main Authors: Gao, Ang (Author), Amitai, Assaf (Author), Doelger, Julia (Author), Chakraborty, Arup K (Author), Julg, Boris D. (Author)
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Department of Chemical Engineering (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor), Massachusetts Institute of Technology. Department of Chemistry (Contributor), Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
Format: Article
Language:English
Published: Elsevier BV, 2021-04-01T17:22:23Z.
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